Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method, comprising: performing following steps through a computer: (1) establishing a multi-dimensional and multi-scale model with infrared spectral features of an object-space target, and extracting an object-space region of interest (ROI) measurement model; (2) performing target detection on an actually measured infrared image captured by an imaging infrared spectrometer, and identifying position information for each ROI of a target; tracking the target, to obtain the target's pixel differences between two frames, and a moving direction of the target, and according to the target's pixel differences between the two frames, performing motion compensation for the target, wherein (2) comprises: (2.1) performing multi-threaded operations to an input actually measured infrared image, comprising: a 1 st thread: performing hyperpixel segmentation to obtain sky background, ground background, target regions, etc., and based on the segmentation result and measurement of area and grayscale features of a target, identifying background regions and taking as a negative sample; a 2 nd thread: extracting full-image Histogram of Oriented Gradient (HOG) features, and distinguish background from the target regions according to a slide-window method, thus obtaining a suspected target and taking as a positive sample; a 3 rd thread: using a full convolutional neural network to detect an input image to obtain a target, and taking it as a positive sample; (2.2) inputting the results obtained by the above threads into a pre-trained support-vector-machine (SVM) classifier, to obtain position information (x, y) of the image of the target; (2.3) creating a Gaussian pyramid by using the image of the target, to obtain multi-scale information of the image and thereafter input it into the trained convolutional neural network (CNN) to obtain pixel differences (Vx, Vy) of respective ROIs of the target with respect to the center position of the target; and (2.4) detecting and obtaining position information of two-frame images of the target according to (2.2) and processing, to obtain the target's frame differences as well as the moving target's direction information from the two-frame images; based on the target's pixel differences between the two frames obtained in (2.3), performing motion compensation for the target; (3) scanning the target identified in (2), and after successfully capturing an image of the target being tracked, controlling an inner framework to point to each target of interest, and according to moving-direction information of the target, performing N-pixel-size motion in a direction shifted by 90° with respect to the moving direction, activating a spectrum measuring module, recording distance information between the measuring device and the target, measuring azimuth information, scale information and time-dimension information, and inputting the information into the multi-dimensional and multi-scale model obtained in (1).
This invention relates to infrared spectral analysis and target tracking for imaging infrared spectrometers. The method addresses challenges in accurately detecting, tracking, and measuring spectral features of moving targets in infrared images, particularly in complex environments with varying backgrounds. The system establishes a multi-dimensional, multi-scale model of an object-space target's infrared spectral features, extracting a region of interest (ROI) measurement model. It then processes an infrared image captured by an imaging spectrometer through parallelized multi-threaded operations. The first thread segments the image into sky, ground, and target regions, using area and grayscale features to identify background (negative samples). The second thread extracts full-image Histogram of Oriented Gradient (HOG) features and applies a sliding-window method to distinguish targets (positive samples). The third thread uses a convolutional neural network (CNN) for target detection. Results from all threads are fed into a pre-trained support-vector-machine (SVM) classifier to determine target position (x, y). The method further creates a Gaussian pyramid from the target image to obtain multi-scale information, inputting it into a trained CNN to compute pixel differences (Vx, Vy) of ROIs relative to the target's center. It tracks the target across frames, calculating frame differences and movement direction. Motion compensation is applied based on pixel differences between frames. After detection, the system scans the target, adjusts an inner framework to point at the target, and performs N-pixel motion perpendicular to the target's movement direction. A spectrum measuring module records distance, azimuth, scale, and time data, feeding this into the multi-dimensional
2. The method of claim 1 , wherein (1) further comprises: (1.1) establishing a three-dimensional model for a target to be measured; (1.2) from the three-dimensional model, determining a ROI section of the target to be measured, and performing material classification to the three-dimensional model of the above-mentioned section to determine a radiation source; (1.3) measuring infrared spectral characteristics of the radiation source, to obtain object-space infrared-spectral-characteristic distribution at a specific angle.
This invention relates to non-destructive testing and material analysis using infrared spectroscopy. The method involves creating a three-dimensional model of a target object to be measured. From this model, a region of interest (ROI) is identified, and material classification is performed on the ROI to determine the radiation source within that section. The method then measures the infrared spectral characteristics of the identified radiation source, capturing the object-space infrared spectral characteristic distribution at a specific angle. This approach enables precise material identification and analysis by leveraging three-dimensional modeling and targeted infrared spectroscopy, improving accuracy in detecting and characterizing materials within a specified region. The technique is particularly useful in applications requiring detailed material analysis without physical contact or destruction of the target object.
4. A measurement device for implementing the method of claim 1 , the device comprising: an industrial computer, a rotary mirror, a beam splitter, a medium-wave lens, a long-wave lens, a non-imaging infrared spectrum measuring unit, and a long-wave infrared imaging unit; wherein: a control interface of the industrial computer is connected to the rotary mirror; the medium-wave lens is mounted on the non-imaging infrared spectrum measuring unit; an output end of the non-imaging infrared spectrum measuring unit is connected to an input end of the industrial computer; the long-wave lens is mounted on the long-wave infrared imaging unit; and an output end of the long-wave infrared imaging unit is connected to the input end of the industrial computer.
This invention relates to a measurement device for analyzing infrared spectra and thermal imaging in industrial or scientific applications. The device addresses the need for simultaneous spectral analysis and thermal imaging to monitor material properties, detect defects, or assess temperature distributions in real-time. Traditional systems often require separate instruments, leading to inefficiencies and misalignment between spectral and imaging data. The device includes an industrial computer that processes data from multiple components. A rotary mirror directs infrared radiation toward either a medium-wave lens or a long-wave lens. The medium-wave lens is mounted on a non-imaging infrared spectrum measuring unit, which captures spectral data in the mid-infrared range. This data is sent to the industrial computer for analysis. The long-wave lens is mounted on a long-wave infrared imaging unit, which generates thermal images in the far-infrared range and transmits them to the industrial computer. The industrial computer controls the rotary mirror to switch between spectral and imaging modes, enabling synchronized data acquisition. The system integrates both functionalities into a single device, improving accuracy and efficiency in applications such as material testing, quality control, and thermal monitoring.
5. The device of claim 4 , wherein the rotary mirror adopts a four-framework servo control and comprises: a reflective mirror, an inner pitch framework, an inner azimuth framework, an outer pitch framework, and an outer azimuth framework, which are sequentially arranged from inside to outside.
A rotary mirror device is used in optical scanning systems to direct and control light beams with high precision. The device addresses the challenge of achieving stable, high-speed beam steering while maintaining accuracy in both pitch and azimuth directions. The rotary mirror incorporates a four-framework servo control system, which enhances stability and responsiveness. The mirror assembly includes a reflective mirror at the center, surrounded sequentially by an inner pitch framework, an inner azimuth framework, an outer pitch framework, and an outer azimuth framework. Each framework is independently controlled to adjust the mirror's orientation in multiple axes. The inner pitch framework allows tilting motion in one direction, while the inner azimuth framework enables rotation around a vertical axis. The outer pitch and azimuth frameworks provide additional stabilization and fine-tuning of the mirror's position. This multi-layered structure improves the mirror's ability to track and redirect light beams with minimal vibration and high precision, making it suitable for applications such as laser scanning, optical communication, and remote sensing. The servo control system ensures real-time adjustments, enhancing the device's performance in dynamic environments.
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November 17, 2020
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